Generative AI - GANs, Diffusion Models & Creative AI |…

Generative AI creates new content including images, text, audio, and v

What is Generative AI?

Generative AI refers to AI systems that can generate new content, such as images, text, music, or video, that is similar to training data but original. These systems learn the underlying distribution of data and can sample from it to create new examples.

Diffusion models learn to reverse a gradual noising process, starting

High-quality generation

Image generation (DALL-E, Midjourney, Stable Diffusion)

live demo · related simulation● LIVE

VAEs learn to encode data into a latent space and decode it back, enab

English universities conduct research on generative models, including GANs, diffusion models, and language generation.

Companies in England develop generative AI solutions and contribute to research on generative models.

Frequently asked questions

What are the key differences between Generative Adversarial Networks (GANs) and Diffusion Models?

GANs use a competitive process between two neural networks – a generator and a discriminator – to create new data, while diffusion models learn to reverse a gradual noising process.

How does controllability in generative AI relate to image generation?

Controllability refers to the ability to guide the creative process of generative AI systems, allowing users to specify desired attributes and styles for generated images.

What is multimodal generation and why is it important in Generative AI?

Multimodal generation involves creating content across multiple modalities – such as text and image – simultaneously, offering a richer and more versatile creative experience.

Why are researchers focused on developing more efficient generative models?

Efficiency in generative models is crucial for reducing computational costs, enabling wider accessibility, and facilitating real-time applications of AI-generated content.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation